The rest of the code is the same as the code used in the {{Codeline|lazy}} example. <br/>

+

Once both results are compared it is quite obvious that for this particular example {{Codeline|ghkss}} is superior to {{Codeline|lazy}}. The TISEAN documentation points out that this is not always the case.

Noise Reduction

This tutorial show different methods of the 'Noise Reduction' section of the TISEAN documentation (located here). It shows the use of simple nonlinear noise reduction (function lazy) and locally projective nonlinear noise reduction (function ghkss). To start let's create noisy data to work with.

Code: Creating a noisy henon map

hen=henon(10000);hen=hen(:,1);# We only need the first columnhen_noisy=hen+std(hen)*0.02.*(-6+sum(rand([size(hen),12]),3));

This created a Henon map contaminated by 2% Gaussian noise à la TISEAN. In the tutorials and exercises on the TISEAN website this would be equivalent to calling makenoise -%2 on the Henon map.
Next we will reduce the noise using simple nonlinear noise reduction lazy.

Code: Simple nonlinear noise reduction

clean=lazy(hen_noisy,7,-0.06,3);# Create delay vectors for both the clean and noisy datadelay_clean=delay(clean);delay_noisy=delay(hen_noisy);# Plot both on one chartplot(delay_noisy(:,1),delay_noisy(:,2),'b.;Noisy Data;','markersize,3,... delay_clean(:,1), delay_clean(:,2), 'r.;CleanData;','markersize,3)

On the chart created the red dots represent cleaned up data. It is much closer to the original than the noisy blue set.
Now we will do the same with ghkss.

Code: Locally projective nonlinear noise reduction

clean=ghkss(hen,'m',7,'q',2,'r',0.05,'k',20,'i',2);

The rest of the code is the same as the code used in the lazy example.
Once both results are compared it is quite obvious that for this particular example ghkss is superior to lazy. The TISEAN documentation points out that this is not always the case.